mem0/docs/get-started/quickstart.mdx

---
title: '🚀 Quickstart'
description: '💡 Start building LLM powered apps under 30 seconds'
---

Embedchain is a Data Platform for LLMs - load, index, retrieve, and sync any unstructured data. Using embedchain, you can easily create LLM powered apps over any data. 

Install embedchain python package:

```bash
pip install embedchain
```

<Tip>
Embedchain now supports OpenAI's latest `gpt-4-turbo` model. Checkout the [docs here](/get-started/faq#how-to-use-gpt-4-turbo-model-released-on-openai-devday) on how to use it.
</Tip>

Creating an app involves 3 steps:

<Steps>
  <Step title="⚙️ Import app instance">
```python
from embedchain import Pipeline as App
app = App()
```
  </Step>
  <Step title="🗃️ Add data sources">
```python
# Add different data sources
app.add("https://en.wikipedia.org/wiki/Elon_Musk")
app.add("https://www.forbes.com/profile/elon-musk")
# You can also add local data sources such as pdf, csv files etc.
# app.add("/path/to/file.pdf")
```
  </Step>
  <Step title="💬 Query or chat or search context on your data">
```python
app.query("What is the net worth of Elon Musk today?")
# Answer: The net worth of Elon Musk today is $258.7 billion.
```
  </Step>
  <Step title="🚀 (Optional) Deploy your pipeline to Embedchain Platform">
```python
app.deploy()
# 🔑 Enter your Embedchain API key. You can find the API key at https://app.embedchain.ai/settings/keys/
# ec-xxxxxx

# 🛠️ Creating pipeline on the platform...
# 🎉🎉🎉 Pipeline created successfully! View your pipeline: https://app.embedchain.ai/pipelines/xxxxx

# 🛠️ Adding data to your pipeline...
# ✅ Data of type: web_page, value: https://www.forbes.com/profile/elon-musk added successfully.
```
  </Step>
</Steps>

Putting it together, you can run your first app using the following code. Make sure to set the `OPENAI_API_KEY` 🔑 environment variable in the code.

```python
import os
from embedchain import Pipeline as App

os.environ["OPENAI_API_KEY"] = "xxx"
app = App()

# Add different data sources
app.add("https://en.wikipedia.org/wiki/Elon_Musk")
app.add("https://www.forbes.com/profile/elon-musk")
# You can also add local data sources such as pdf, csv files etc.
# app.add("/path/to/file.pdf")

response = app.query("What is the net worth of Elon Musk today?")
print(response)
# Answer: The net worth of Elon Musk today is $258.7 billion.

app.deploy()
# 🔑 Enter your Embedchain API key. You can find the API key at https://app.embedchain.ai/settings/keys/
# ec-xxxxxx

# 🛠️ Creating pipeline on the platform...
# 🎉🎉🎉 Pipeline created successfully! View your pipeline: https://app.embedchain.ai/pipelines/xxxxx

# 🛠️ Adding data to your pipeline...
# ✅ Data of type: web_page, value: https://www.forbes.com/profile/elon-musk added successfully.
```

You can try it out yourself using the following Google Colab notebook:

<a href="https://colab.research.google.com/drive/17ON1LPonnXAtLaZEebnOktstB_1cJJmh?usp=sharing">
    <img src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open in Colab" />
</a>